Mistral AI released Mixtral, an open-source Mixture-of-Experts (MoE) model outperforming GPT-3.5. Fireworks AI improved MoE model efficiency with FP16 and FP8-based FireAttention, greatly enhancing speed. Despite limitations of quantization methods, Fireworks FP16 and FP8 implementations show superior performance, reducing model size and improving requests/second. This research marks a significant advancement in efficient MoE model serving.
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Mixture-of-Experts (MoE) and FireAttention by Fireworks AI
Introduction
Mixture-of-Experts (MoE) is an architecture that utilizes multiple individual machine learning (ML) models to solve complex tasks. To enhance MoE capabilities, Fireworks AI introduced FireAttention, a custom CUDA kernel optimized for Multi-Query Attention Models, which significantly improves efficiency and performance tradeoff.
FireAttention Features
FireAttention leverages FP16 and FP8-based serving stack, providing four times better speed-up compared to other open-source software. It is particularly effective in handling non-uniform distribution of LLM activations, offering flexibility and efficiency during the model’s generation process.
Performance Evaluation
Fireworks AI conducted a comprehensive evaluation of the Mixtral model using a prompt length of 1K and 50 generated tokens, covering various use cases. The model demonstrated superior performance in language understanding, measured using the MMLU metric, and showcased improved latency and throughput metrics.
Conclusion and Practical Implications
The FireAttention FP16 and FP8 implementations represent a significant advancement in serving MoE models like Mixtral, providing a remarkable tradeoff for accuracy and performance. FP8 specifically offers a twofold reduction in model size and a corresponding improvement in effective requests/second, highlighting its superiority over previous quantization methods. This development signifies a substantial step towards more efficient serving for MoE models with minimal impact on quality.
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